{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7e2317c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aiscuser/.conda/envs/react/lib/python3.7/site-packages/clip/clip.py:24: UserWarning: PyTorch version 1.7.1 or higher is recommended\n",
      "  warnings.warn(\"PyTorch version 1.7.1 or higher is recommended\")\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "from tqdm import tqdm\n",
    "\n",
    "import faiss\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import clip\n",
    "import numpy as np\n",
    "import os\n",
    "import shutil\n",
    "\n",
    "from react_retrieval.utils.prompts import *\n",
    "from react_retrieval.data.filelist_controller import FileListController\n",
    "\n",
    "from autofaiss.utils.decorators import Timeit\n",
    "\n",
    "import logging\n",
    "\n",
    "\n",
    "def setup_logging(logging_level: int):\n",
    "    \"\"\"Setup the logging.\"\"\"\n",
    "    logging.config.dictConfig(dict(version=1, disable_existing_loggers=False))\n",
    "    logging_format = \"%(asctime)s [%(levelname)s]: %(message)s\"\n",
    "    logging.basicConfig(level=logging_level, format=logging_format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89ab0b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image as IPImage\n",
    "from IPython.display import display\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eba3ce60",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_argument_parser():\n",
    "    parser = argparse.ArgumentParser(description='Extract features from Vision-Language datasets.')\n",
    "    parser.add_argument('--model', type=str, default='vitb32')\n",
    "    parser.add_argument('--metafile', type=str)\n",
    "    parser.add_argument('--scatter_dir', type=str)\n",
    "    parser.add_argument('--faiss_index', type=str)\n",
    "    parser.add_argument('--output_dir', type=str)\n",
    "    parser.add_argument('--dataset', type=str, help='ICinW dataset name.')\n",
    "    parser.add_argument('--images_per_class', type=int, default=50, help='number of images per class')\n",
    "    return parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a471a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "args = get_argument_parser().parse_args([\n",
    "    \"--metafile\", \"../data/laion-400m/processed/filelist.pkl\",\n",
    "    \"--scatter_dir\", \"../data/laion-400m/scattered\",\n",
    "    \"--faiss_index\", \"../data/laion-400m/processed/image.index\",\n",
    "    \"--output_dir\", \"../data/laion-400m/retrieval\",\n",
    "    \"--dataset\", \"caltech-101\",\n",
    "    \"--images_per_class\", \"200\",\n",
    "])\n",
    "\n",
    "setup_logging(logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b696b231",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-24 02:56:35,656 [INFO]: Loading index: ../data/laion-400m/processed/image.index 02/24/2023, 02:56:35\n",
      "2023-02-24 02:58:33,793 [INFO]: >>> Finished \"Loading index: ../data/laion-400m/processed/image.index\" in 118.1357 secs\n"
     ]
    }
   ],
   "source": [
    "with Timeit(f\"Loading index: {args.faiss_index}\"):\n",
    "    index = faiss.read_index(args.faiss_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "193e16e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-24 02:58:33,805 [INFO]: Loading file list controller: ../data/laion-400m/processed/filelist.pkl 02/24/2023, 02:58:33\n",
      "2023-02-24 03:00:05,829 [INFO]: >>> Finished \"Loading file list controller: ../data/laion-400m/processed/filelist.pkl\" in 92.0231 secs\n"
     ]
    }
   ],
   "source": [
    "with Timeit(f\"Loading file list controller: {args.metafile}\"):\n",
    "    file_list_controller = FileListController(args.metafile, args.scatter_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a21caf2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-02-24 03:00:15,449 [INFO]: Loading model: ViT-B/32 02/24/2023, 03:00:15\n",
      "2023-02-24 03:00:45,799 [INFO]: >>> Finished \"Loading model: ViT-B/32\" in 30.3486 secs\n"
     ]
    }
   ],
   "source": [
    "class_names = class_map[args.dataset]\n",
    "templates = template_map[args.dataset]\n",
    "\n",
    "model_name = {\n",
    "    \"vitb32\": \"ViT-B/32\",\n",
    "    \"vitb16\": \"ViT-B/16\",\n",
    "    \"vitl14\": \"ViT-L/14\",\n",
    "}[args.model]\n",
    "\n",
    "with Timeit(f\"Loading model: {model_name}\"):\n",
    "    if model_name in clip.available_models():\n",
    "        model, preprocess = clip.load(model_name)\n",
    "    model.cuda().eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f25fad9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def query_by_text(texts, model, index, k):\n",
    "    assert type(texts) is list, \"Please wrap texts into List[str].\"\n",
    "\n",
    "    texts = clip.tokenize(texts, context_length=77, truncate=True).cuda()\n",
    "    with torch.no_grad():\n",
    "        text_embeddings = model.encode_text(texts)\n",
    "        text_embeddings = F.normalize(text_embeddings)\n",
    "\n",
    "    queries = text_embeddings.data.cpu().numpy().astype('float32')\n",
    "    dists, knns = index.search(queries, k)\n",
    "    \n",
    "    return dists, knns\n",
    "\n",
    "dists, knns = query_by_text(['a professional photo of a dog'], model, index, k=args.images_per_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ee492fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "purebred golden retriever sitting in front of a white background\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_file, text_file = file_list_controller[knns[0, 8]]\n",
    "image = IPImage(filename=image_file)\n",
    "print(open(text_file, 'r').read())\n",
    "display(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cf1f5637",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_retrieved_images(knns, output_dir):\n",
    "    for idx in range(knns.shape[0]):\n",
    "        subdir = os.path.join(output_dir, f'{idx:03d}')\n",
    "        os.makedirs(subdir, exist_ok=True)\n",
    "        for knn_idx in knns[idx]:\n",
    "            image_file, text_file = file_list_controller[knn_idx]\n",
    "            shutil.copy2(image_file, os.path.join(subdir, os.path.basename(image_file)))\n",
    "            shutil.copy2(text_file, os.path.join(subdir, os.path.basename(text_file)))\n",
    "\n",
    "collect_retrieved_images(knns, output_dir=os.path.join(args.output_dir, args.dataset, f'{args.images_per_class}nn', 'dog'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5227566d",
   "metadata": {},
   "source": [
    "# I2I retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ed5edc2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEPOWER Metal Desk Lamp, Eye-Caring Table Lamp, Study Lamps with Flexible Goose Neck for Bedroom and Office ( Sandy Black)\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def retrieve_i2i(image_file, k=1):\n",
    "    image = Image.open(image_file)\n",
    "    display(IPImage(filename=image_file))\n",
    "    \n",
    "    image = preprocess(image).cuda().unsqueeze(0)\n",
    "    image_embeddings = model.encode_image(image)\n",
    "    image_embeddings = F.normalize(image_embeddings)\n",
    "\n",
    "    queries = image_embeddings.data.cpu().numpy().astype('float32')\n",
    "    dists, knns = index.search(queries, k)\n",
    "\n",
    "    image_file, text_file = file_list_controller[knns[0, 0]]\n",
    "    image = IPImage(filename=image_file)\n",
    "    print(open(text_file, 'r').read())\n",
    "    display(image)\n",
    "\n",
    "retrieve_i2i('./src_images/lamp.jpg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
